Fingerprint matching using virtual minutiae

ABSTRACT

A fingerprint matching system for fingerprint matching using virtual minutiae includes a fingerprint scanning device, including (i) an interface to a fingerprint scanner, and (ii) a sensor associated with the fingerprint scanner, configured to identify a match between a search fingerprint representing a fingerprint of a subject and a reference fingerprint from among a plurality of reference fingerprints representing fingerprints of a plurality of historical subjects; a database containing the plurality of reference fingerprints representing fingerprints of the plurality of historical subjects; and a computing device in communication with said database and said fingerprint scanning device.

FIELD

The present disclosure relates generally to fingerprint identificationsystems.

BACKGROUND

Identification pattern systems, such as fingerprint identificationsystems, are commonly used in many contexts to efficiently identifyindividuals and to verify the identity of individuals. For example, inthe public sector, fingerprint identification systems are often acrucial element in most modern criminal investigations. Similarly, inthe private sector, fingerprint identification systems are often used toaddress security concerns or to detect credit card or personal identityfraud.

Fingerprint matching depends, in part, on matching characteristicsbetween reference a fingerprint and a search fingerprint. For instance,reference fingerprints are extracted from a repository of previouslyobtained fingerprint records that are used to facilitate a match and thesearch fingerprint is a fingerprint associated with the individual forwhom identification or identity verification is sought. In manyexamples, the quality and accuracy of the fingerprint matching dependson the quality and availability of matching characteristics.

SUMMARY

In some fingerprint matching applications, due to operational, storage,or processing constraints, only fingerprint minutiae information may beavailable as matching characteristics for fingerprint matching. Whilematching may be achieved using fingerprint minutiae information, in someexamples, the accuracy of such matches may be less than desirable.Accordingly, methods and systems to improve the accuracy of fingerprintmatching in such situations are desirable.

Implementations may include one or more of the following features. Forexample, a method for fingerprint matching using virtual minutiae mayinclude: receiving a first similarity score between a list of searchminutiae from a search fingerprint and a list of file minutiae from areference fingerprint; generating (i) a list of virtual search minutiaefor the list of search minutiae, and (ii) a list of virtual fileminutiae for the list of file minutiae; computing a second similarityscore based at least on comparing the list of virtual search minutiaeand the list of virtual file minutiae; computing a fused similarityscore between the search fingerprint and the reference fingerprint basedat least on combining the first similarity score and the secondsimilarity score; and providing the fused similarity score for output tothe automatic fingerprint identification system to determine thefingerprint match.

Other versions include corresponding systems, and computer programs,configured to perform the actions of the methods encoded on computerstorage devices.

One or more implementations may include the following optional features.For example, in some implementations, the method may include: retrieving(i) a search fingerprint from a fingerprint scanning device containing alist of search minutiae, and (ii) a reference fingerprint from adatabase containing a list of file minutiae; generating (i) a set ofsearch octant feature vectors for each search minutia from the list ofsearch minutiae, and (ii) a set of file octant feature vectors for eachfile minutia from the list of file minutiae; generating a plurality ofpossible mated minutiae based at least on pairing an octant neighborhoodof each of the list of search minutiae with the octant neighborhood ofeach of the list of file minutiae, where the octant neighborhoodincludes a combination of (i) a neighborhood formed between the list ofvirtual search minutiae and the list of list of search minutiae, and(ii) a neighborhood formed between the list of virtual file minutiae andthe list of list of file minutiae, and each of the plurality of possiblemated minutiae includes (i) a search minutiae from the list of searchminutiae, and (ii) a file minutiae from the list of file minutiae; anddetermining a first similarity score between the list of search minutiaeand the list of file minutiae based at least on comparing the pluralityof possible mated minutiae.

In some implementations, the method may include: retrieving (i) a searchfingerprint from a fingerprint scanning device containing a list ofsearch minutiae, and (ii) a reference fingerprint from a databasecontaining a list of file minutiae; identifying an overlapping regioncontaining a portion of the search fingerprint and a portion of thereference fingerprint; applying a coordinate grid including a pluralityof cross points to the overlapping region; placing (i) a virtual searchminutia for the list of search minutiae and (ii) a virtual file minutiafor the list of file minutiae, on each of the plurality of cross pointsof the applied regular grid over the overlapping region; and generating(i) a set of virtual search octant feature vectors for each virtualsearch minutia from the list of virtual search minutiae, and (ii) a setof virtual file octant feature vectors for each virtual file minutiafrom the list of virtual file minutiae.

In some implementations, generating the list of virtual minutiae foreach of the list of search minutiae and the list of file minutiae isbased at least on a density of the plurality of cross points within theoverlapping region.

In some implementations, computing the second similarity score comprisescomputing a second similarity score using a local matching process thatincludes: obtaining, for each virtual search minutia from the list ofvirtual search minutiae, a corresponding virtual file minutia from thelist of virtual file minutiae; computing, for each virtual searchminutiae and its respective corresponding virtual file minutiae, a localsimilarity score between the virtual search minutiae and thecorresponding virtual file minutiae based at least on an octantneighborhood; and generating the second similarity score for the list ofvirtual search minutiae based at least on aggregating the respectivelocal similarity scores for each of the virtual search minutiae withinthe list of virtual search minutiae, where the octant neighborhoodincludes a combination of (i) a neighborhood formed between the list ofvirtual search minutiae and the list of list of search minutiae, and(ii) a neighborhood formed between the list of virtual file minutiae andthe list of list of file minutiae.

In some implementations, combining the first similarity score and thesecond similarity score includes assigning respective weights to eachthe first similarity score and the second similarity score.

In some implementations, assigning the respective weights to each of thefirst and second similarity scores includes assigning a respectivelinear weights to each of the first and second similarity scores.

In some implementations, assigning the respective weights to each of thefirst and second similarity scores includes assigning a respectivenon-linear weights to each of the first and second similarity scores.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other potentialfeatures and advantages will become apparent from the description, thedrawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of an exemplary automatic fingerprintidentification system.

FIG. 1B is a block diagram of an exemplary feature extraction process.

FIG. 2 is an exemplary illustration of geometric relationships between areference minutia and a neighboring minutia.

FIG. 3 is a graphical illustration of the relationships represented inan exemplary octant feature vector (OFV).

FIG. 4 is an exemplary process of generating an octant feature vector(OFV).

FIG. 5 is an exemplary process of calculating similarity between twominutiae.

FIG. 6 is an exemplary alignment process for a pair of fingerprints.

FIG. 7 is an exemplary minutiae matching process for a pair offingerprints.

FIG. 8 is an exemplary method for analyzing the similarity between asearch fingerprint and a file fingerprint based on virtual minutiae.

FIG. 9 is an exemplary set of virtual minutiae.

FIG. 10 is an exemplary method of matching fingerprints using virtualminutiae.

DETAILED DESCRIPTION OF THE INVENTION

System Architecture

FIG. 1 is a block diagram of an exemplary automatic fingerprintidentification system 100. Briefly, the automatic fingerprintidentification system 100 may include a computing device including amemory device 110, a processor 115, a presentation interface 120, a userinput interface 130, and a communication interface 135. The automaticfingerprint identification system 100 may be configured to facilitateand implement the methods described through this specification. Inaddition, the automatic fingerprint identification system 100 mayincorporate any suitable computer architecture that enables operationsof the system described throughout this specification.

The processor 115 may be operatively coupled to memory device 110 forexecuting instructions. In some implementations, executable instructionsare stored in the memory device 110. For instance, the automaticfingerprint identification system 100 may be configurable to perform oneor more operations described by programming the processor 115. Forexample, the processor 115 may be programmed by encoding an operation asone or more executable instructions and providing the executableinstructions in the memory device 110. The processor 115 may include oneor more processing units, e.g., without limitation, in a multi-coreconfiguration.

The memory device 110 may be one or more devices that enable storage andretrieval of information such as executable instructions and/or otherdata. The memory device 110 may include one or more tangible,non-transitory computer-readable media, such as, without limitation,random access memory (RAM), dynamic random access memory (DRAM), staticrandom access memory (SRAM), a solid state disk, a hard disk, read-onlymemory (ROM), erasable programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. Theabove memory types are exemplary only, and are thus not limiting as tothe types of memory usable for storage of a computer program.

The memory device 110 may be configured to store a variety of dataincluding, for example, matching algorithms, scoring algorithms, scoringthresholds, perturbation algorithms, fusion algorithms, virtual minutiaegeneration algorithms, minutiae overlap analysis algorithms, and/orvirtual minutiae analysis algorithms. In addition, the memory device 110may be configured to store any suitable data to facilitate the methodsdescribed throughout this specification.

The presentation interface 120 may be coupled to processor 115. Forinstance, the presentation interface 120 may present information, suchas a user interface showing data related to fingerprint matching, to auser 102. For example, the presentation interface 120 may include adisplay adapter (not shown) that may be coupled to a display device (notshown), such as a cathode ray tube (CRT), a liquid crystal display(LCD), an organic LED (OLED) display, and/or a hand-held device with adisplay. In some implementations, the presentation interface 120includes one or more display devices. In addition, or alternatively, thepresentation interface 120 may include an audio output device (notshown), e.g., an audio adapter and/or a speaker.

The user input interface 130 may be coupled to the processor 115 andreceives input from the user 102. The user input interface 130 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,and/or a touch sensitive panel, e.g., a touch pad or a touch screen. Asingle component, such as a touch screen, may function as both a displaydevice of the presentation interface 120 and the user input interface130.

In some implementations, the user input interface 130 may represent afingerprint scanning device that is used to capture and recordfingerprints associated with a subject (e.g., a human individual) from aphysical scan of a finger, or alternately, from a scan of a latentprint. In addition, the user input interface 130 may be used to create aplurality of reference records.

A communication interface 135 may be coupled to the processor 115 andconfigured to be coupled in communication with one or more other devicessuch as, for example, another computing system (not shown), scanners,cameras, and other devices that may be used to provide biometricinformation such as fingerprints to the automatic fingerprintidentification system 100. Such biometric systems and devices may beused to scan previously captured fingerprints or other image data or tocapture live fingerprints from subjects. The communication interface 135may include, for example, a wired network adapter, a wireless networkadapter, a mobile telecommunications adapter, a serial communicationadapter, and/or a parallel communication adapter. The communicationinterface 135 may receive data from and/or transmit data to one or moreremote devices. The communication interface 135 may be also beweb-enabled for remote communications, for example, with a remotedesktop computer (not shown).

The presentation interface 120 and/or the communication interface 135may both be capable of providing information suitable for use with themethods described throughout this specification, e.g., to the user 102or to another device. In this regard, the presentation interface 120 andthe communication interface 135 may be used to as output devices. Inother instances, the user input interface 130 and the communicationinterface 135 may be capable of receiving information suitable for usewith the methods described throughout this specification, and may beused as input devices.

The processor 115 and/or the memory device 110 may also be operativelycoupled to the database 150. The database 150 may be anycomputer-operated hardware suitable for storing and/or retrieving data,such as, for example, pre-processed fingerprints, processedfingerprints, normalized fingerprints, extracted features, extracted andprocessed feature vectors such as octant feature vectors (OFVs),threshold values, virtual minutiae lists, minutiae lists, matchingalgorithms, scoring algorithms, scoring thresholds, perturbationalgorithms, fusion algorithms, virtual minutiae generation algorithms,minutiae overlap analysis algorithms, and virtual minutiae analysisalgorithms.

The database 150 may be integrated into the automatic fingerprintidentification system 100. For example, the automatic fingerprintidentification system 100 may include one or more hard disk drives thatrepresent the database 150. In addition, for example, the database 150may include multiple storage units such as hard disks and/or solid statedisks in a redundant array of inexpensive disks (RAID) configuration. Insome instances, the database 150 may include a storage area network(SAN), a network attached storage (NAS) system, and/or cloud-basedstorage. Alternatively, the database 150 may be external to theautomatic fingerprint identification system 100 and may be accessed by astorage interface (not shown). For instance, the database 150 may beused to store various versions of reference records including associatedminutiae, octant feature vectors (OFVs) and associated data related toreference records.

Feature Extraction

In general, feature extraction describes the process by which theautomatic fingerprint identification system 100 extracts a list ofminutiae from each of reference fingerprint, and the search fingerprint.As described, a “minutiae” represent major features of a fingerprint,which are used in comparisons of the reference fingerprint to the searchfingerprint to determine a fingerprint match. For example, common typesof minutiae may include, for example, a ridge ending, a ridgebifurcation, a short ridge, an island, a ridge enclosure, a spur, acrossover or bridge, a delta, or a core.

FIG. 1B is a block diagram of an exemplary feature extraction process150. As shown, after receiving an input fingerprint 104, the automaticfingerprint identification system 100 initially identifies a set offeatures 112 within the fingerprint, generates a list of minutiae 114,and extracts a set of feature vectors 116. For instance, the automaticfingerprint identification system 100 may generate a list of minutia 114for each of the reference fingerprint (or “reference record”) and asearch fingerprint (or “search fingerprint”).

In some implementations, the feature vectors 116 may be described usingfeature vector that is represented by Mf_(i)=(x_(i),y_(i),θ_(i)). Asdescribed, the feature vector Mf_(i) includes a minutia location that isdefined by coordinate geometry such as (x_(i),y_(i)), and a minutiaedirection that is defined by the angle θ_(i)ϵ[0,2π]). In other examples,further minutiae characteristics such as, quality, ridge frequency, andridge curvature may also be used to describe feature vector Mf_(i). Theextracted feature vectors may be used to generate octant feature vectors(OFVs) for each of identified minutia within the search and referencefingerprints.

Octant Feature Vector (OFV) Overview

The automatic fingerprint identification system 100 may compare thesearch and reference records based on initially generating featurevectors associated with minutiae that are extracted from the search andreference records, respectively. For instance, as described throughoutthis specification, in some implementations, octant feature vectors(OFVs) may be used to as feature vectors that define attributes of theextracted minutiae. However, in other implementations, other minutiaedescriptors may be used.

OFVs encode geometric relationships between reference minutiae and thenearest neighboring minutiae to the reference minutiae in a particularsector (referred to as the “octant neighborhood”) of the octant. Eachsector of the octant used in an OFV spans 45 degrees of a fingerprintregion. The nearest neighboring minutiae may be assigned to one sectorof the octant based on their orientation difference. The geometricrelationship between a reference minutia and its nearest minutia in eachoctant sector may be described by relative features including, forexample, distance between the minutiae and the orientation differencebetween minutiae. The representation achieved by the use of an OFV isinvariant to transformation.

Pairs of reference minutiae and nearest neighboring minutiae may beidentified as “mated minutiae pairs.” The mated minutiae pairs in areference record and a search fingerprint may be identified by comparingthe respective OFVs of minutiae extracted from the reference record andthe search fingerprint. The transformation parameters may be estimatedby comparing attributes of the corresponding mated minutiae. Forexample, the transformation parameters may indicate the degree to whichthe search fingerprint has been transformed (e.g., perturbed or twisted)as relative to a particular reference record. In other examples, thetransformation parameters may be applied to verify that, for aparticular pair of a reference record and a search fingerprint (a“potential matched fingerprint pair”), mated minutiae pairs exhibitcorresponding degrees of transformation. Based on the amount ofcorresponding mated minutiae pairs in each potential matched fingerprintpair, and the consistency of the transformation, a similarity score maybe assigned. In some implementations, the pair of potential matchedfingerprint pairs with the highest similarity score may be determined asa candidate matched fingerprint pair.

The automatic fingerprint identification system 100 may calculate an OFVfor each minutia that encodes the geometric relationships between thereference minutia and its nearest minutiae in each sector of the octant.For instance, the automatic fingerprint identification system 100 maydefine eight octant sectors and assigns the nearest minutiae to onesector of the octant based on the location of each minutia within thesectors. The geometric relationship between a reference minutia and itsnearest minutia in each octant sector is represented by the relativefeatures. For example, in some implementations, the OFV encodes thedistance, the orientation difference, and the ridge count differencebetween the reference feature and the nearest neighbor features. Becausethe minutia orientation can flexibly change up to 45° due to the octantsector approach, relative features are independent from anytransformation.

The automatic fingerprint identification system 100 may use the OFVs todetermine the number of possible corresponding minutiae pairs.Specifically, the automatic fingerprint identification system 100 mayevaluate the similarity between two respective OFVs associated with thesearch fingerprint and the reference fingerprint. The automaticfingerprint identification system 100 may identify all possible localmatching areas of the compared fingerprints by comparing the OFVs. Theautomatic fingerprint identification system 100 may also an individualsimilarity score for each of the mated OFV pairs.

The automatic fingerprint identification system 100 may cluster all OFVsof the matched areas with similar transformation effects (e.g., rotationand transposition) into an associated similar bin. Note that theprecision of the clusters of the bins (e.g., the variance of the similarrotations within each bin) is a proxy for the precision of this phase.Automatic fingerprint identification system 100 therefore uses bins withhigher numbers of matched OFVs (e.g., clusters with the highest countsof OFVs) for the first phase global alignment.

The automatic fingerprint identification system 100 may use the locationand angle of each selected bin as the parameters of a reference point(an “anchor point”) to perform a global alignment procedure. Morespecifically, the automatic fingerprint identification system 100 mayidentify the global alignment based on the bins that include thegreatest number of the global mated minutiae pairs, and the location andangle associated with each of those bins. Based on the number of globalpaired minutiae found and the total of individual similarity scorescalculated for the corresponding OFVs within the bin or bins, theautomatic fingerprint identification system 100 may identify thetransformations (e.g., the rotations of the features) with the bestalignment.

In a second phase, the automatic fingerprint identification system 100performs a more precise pairing using the transformations with the bestalignment to obtain a final set of the globally aligned minutiae pairs.In this phase, automatic fingerprint identification system 100 performsa pruning procedure to find geometrically consistent minutiae pairs withtolerance of distortion for each aligned minutiae set that factors inthe local and global geometrical index consistency. By performing suchalignment globally and locally, automatic fingerprint identificationsystem 100 determines the best set of global aligned minutiae pairs.Automatic fingerprint identification system 100 uses the associatedmini-scores of the global aligned pairs to calculate the globalsimilarity score. Furthermore, automatic fingerprint identificationsystem 100 factors in a set of absolute features of the minutiae,including the quality, ridge frequency, and the curvatures in thecomputation of the final similarity score.

OFV Generation

The automatic fingerprint identification system 100 may generate anoctant feature vector (OFV) for each minutia of the features extracted.Specifically, as described above, the automatic fingerprintidentification system 100 may generate OFVs encoding the distance andthe orientation difference between the reference minutiae and thenearest neighbor in each of eight octant sectors. Alternately, theautomatic fingerprint identification system 100 may generate featurevectors with different numbers of sectors.

FIG. 2 is an exemplary illustration 200 of geometric relationshipsbetween a reference minutia 210 and a neighboring minutia 220. Thegeometric relationships may be used to construct a rotation andtranslation invariant feature vector that includes relative attributes(d_(ij),α_(ij),β_(ij)) between the reference minutia 210 and theneighboring minutia 220.

As depicted in FIG. 2, the automatic fingerprint identification system100 may compute a Euclidean distance 230 between the reference minutia210 and the neighboring minutia 220, a minimum rotation angle 240 forthe neighboring minutia, and a minimum rotation angle 250 for thereference minutia 210. In addition, the automatic fingerprintidentification system may compute a ridge count 260 across the referenceminutia 210 and the neighboring minutia 220.

Specifically, the rotation and translation invariant feature vector maybe represented as vector 1 (represented below). In some implementations,an OFV is created to describe the geometric relationship between.Further, M_(i) represents a reference minutia and M_(j) represents anearest neighbor minutiae in one of the octant sectors. The OFV for eachsector may be described in the given vector from vector 1:

Vector 1: (d_(ij),α_(ij),β_(ij)),

where d_(ij) denotes the Euclidean distance 230,

where α_(ij)=λ(θ_(i),θ_(j)) denotes the minimum rotation angle 240required to rotate a line of direction θ_(i) in a particular direction(e.g., counterclockwise in FIG. 2) to make the line parallel with a lineof direction θ_(j), and

where β_(ij)=λ(θ_(i),∠(M_(i),M_(j))) denotes the minimum rotation angle250, where ∠(M_(i),M_(j)) denotes the direction from the referenceminutia 210 to the neighboring minutia 220, and where λ(a,b) denotes thesame meaning as defined in α_(ij)

Specifically, because each element calculated in the feature vector is arelative measurement between the reference minutia 210 and theneighboring minutia 220, the feature vector is independent from therotation and translation of the fingerprint. Elements 230, 240, and 250may be referred to as relative features and are used to compute thesimilarity between pair of reference minutia 210 and the neighboringminutia 220. In some implementations, other minutiae features such asabsolute features may additionally or alternatively be used by theautomatic fingerprint identification system 100 to weight the computedsimilarity score between a pair of mated minutiae that includesreference minutia 210 and the neighboring minutia 220.

FIG. 3 is a graphical illustration of the relationships represented inan exemplary octant feature vector (OFV) 300. The OFV 300 may begenerated for a reference minutia 310, which corresponds to thereference minutia 210 as shown in FIG. 2. As shown, the OFV 300represents relationships between the reference minutiae 310 and itsnearest neighboring minutiae 322, 332, 342, 352, 362, 372, 382, and 392in sectors 320, 330, 340, 350, 360, 370, 380, and 390, respectively.However, the graphical illustration of OFV 300 does not depict thedetails of the geographic relationships, which are described withinrespect to FIG. 2. Although FIG. 3 indicates a neighboring minutiawithin each octant sector, in some instances, there may be noneighboring minutiae within a particular sector. In such instances, theOFV for the particular sector without a neighboring minutia is set tozero. Otherwise, because the neighboring minutiae 322, 332, 342, 352,362, 372, 382, and 392 may not overlap with reference minutiae 310, theOFV is greater than zero.

FIG. 4 is an exemplary process 400 of generating an octant featurevector (OFV). Briefly, the process 400 may include identifying aplurality of minutiae from the input fingerprint image (410), selectinga particular minutia from the plurality of minutiae (420), defining aset of octant sectors for the plurality of minutiae (430), assigningeach of the plurality of minutiae to an octant sector (440), identifyinga neighboring minutiae to the particular minutia for each octant sector(450), and generating an octant feature vector for the particularminutia (460).

In more detail, the process 400 may include the process may includeidentifying a plurality of minutiae from the input fingerprint image(410). For instance, the automatic fingerprint identification system 100may receive the input fingerprint 402 and generate a list of minutiae412 using the techniques described previously with respect to FIG. 1B.

The process 400 may include selecting a particular minutia from theplurality of minutiae (420). For instance, the automatic fingerprintidentification system 100 may select a particular minutia within thelist of minutiae 412.

The process 400 may include defining a set of octant sectors for theplurality of minutiae (430). For instance, the automatic fingerprintidentification system 100 may generate a set of octant sectors 432 thatinclude individual octant sectors k₀ to k₇ as shown in FIG. 4. The setof octant sectors 432 may be generated in reference to the particularminutia that is selected in step 420.

The process 400 may include assigning each of the plurality of minutiaeto an octant sector (440). For instance, the automatic fingerprintidentification system 100 may assign each of the plurality of minutiaefrom the list of minutiae 412 into corresponding octant sectors withinthe set of octant sectors 432. The assigned minutiae may be associatedwith the corresponding octant sectors in a list 442 that includes thenumber of minutiae that are identified within each individual octantsector. For example, as shown in FIG. 4, the exemplary octant sector k₁has no identified minutiae, whereas the exemplary k₆ includes twoidentified minutiae within the octant sector. The graphical illustration444 represents the locations of the plurality of minutiae, relative tothe particular selected minutia, M_(i), within the individual octantsectors.

The process 400 may include identifying a neighboring minutia to theparticular minutia for each octant sector (450). For instance, theautomatic fingerprint identification system 100 may identify, from allthe neighboring minutiae within each octant sector, the neighboringminutia that is the closest neighboring minutia based on the distancebetween each neighboring minutia and the particular selected minutia,M_(i). For example, for the octant sector k₆, the automatic fingerprintidentification system 100 may determine that the minutia, M₆ is theclosest neighboring minutia based on the distance between M_(i) and M₆.The closest neighboring minutiae for all of the octant sectors may beaggregated within a list of closest neighboring minutiae 452 thatidentifies each of the closest neighboring minutiae.

The process 400 may include generating an octant feature vector for theparticular minutia (460). For instance, the automatic fingerprintidentification system 100 may generate an octant feature vector 462,based on the list of closest neighboring minutiae 452, which includes aset of relative features such as the Euclidean distance 230, the minimumrotation angle 240, and the minimum rotation angle 250 as describedpreviously with respect to FIG. 2.

As described above with respect to FIGS. 2-4, OFVs for minutiae may beused to characterize local relationships with neighboring minutiae,which are invariant to the rotation and translation of the fingerprintthat includes the minutiae. The OFVs are also insensitive to distortion,since the nearest neighboring minutiae are assigned to multiple octantsectors in various directions, thereby allowing flexibility oforientation of up to 45°. In this regard, the OFVs of minutiae within afingerprint may be compared against the OFVs of minutiae within anotherfingerprint (e.g., a search fingerprint) to determine a potential matchbetween the two fingerprints. Descriptions of the general fingerprintmatching process, and the OFV matching process are provided below.

Fingerprint Identification and Matching

In general, the automatic fingerprint identification system 100 mayperform fingerprint identification and matching in two stages: (1) anenrollment stage, and (2) an identification/verification stage.

In the enrollment stage, an individual (or a “registrant”) has theirfingerprints and personal information enrolled. The registrant may be anindividual manually providing their fingerprints for scanning or,alternately, an individual whose fingerprints were obtained by othermeans. In some examples, registrants may enroll fingerprints usinglatent prints, libraries of fingerprints, and any other suitablerepositories and sources of fingerprints. As described, the process of“enrolling” and other related terms refer to providing biometricinformation (e.g., fingerprints) to an identification system (e.g., theautomatic fingerprint identification system 100).

The automatic fingerprint identification 100 system may extract featuressuch as minutiae from fingerprints. As described, “features” and relatedterms refer to characteristics of biometric information (e.g.,fingerprints) that may be used in matching, verification, andidentification processes. The automatic fingerprint identificationsystem 100 may create a reference record using the personal informationand the extracted features, and save the reference record into thedatabase 150 for subsequent fingerprint matching, verification, andidentification processes.

In some implementations, the automatic fingerprint identification system100 may contain millions of reference records. As a result, by enrollinga plurality of registrants (and their associated fingerprints andpersonal information), the automatic fingerprint identification system100 may create and store a library of reference records that may be usedfor comparison to search fingerprints. The library may be stored at thedatabase 150 associated.

In the identification stage, the automatic fingerprint identificationsystem 100 may use the extracted features and personal information togenerate a record known as a “search fingerprint”. The searchfingerprint represents a source fingerprint for which identification issought. For example, in criminal investigations, a search fingerprintmay be retrieved from a latent print at a crime scene. The automaticfingerprint identification may compare the search fingerprint with theenrolled reference records in the database 150. For example, during asearch procedure, a search fingerprint may be compared against thereference records stored in the database 150. In such an example, thefeatures of the search fingerprint may be compared to the features ofeach of the plurality of reference records. For instance, minutiaeextracted from the search fingerprint may be compared to minutiaeextracted from each of the plurality of reference records.

As described, a “similarity score” is a measurement of the similarity ofthe fingerprint features (e.g., minutiae) between the search fingerprintand each reference record, represented as a numerical value to degree ofsimilarity. For instance, in some implementations, the values of thesimilarity score may range from 0.0 to 1.0, where a higher magnituderepresents a greater degree of similarity between the search fingerprintand the reference record.

The automatic fingerprint identification system 100 may computeindividual similarity scores for each comparison of features (e.g.,minutiae), and aggregate similarity scores (or “match scores”) betweenthe search fingerprint to each of the plurality of reference records. Inthis regard, the automatic fingerprint identification system 100 maygenerate similarity scores of varying levels of specificity throughoutthe matching process of the search fingerprint and the plurality ofreference records.

The automatic fingerprint identification system 100 may also sort eachof the individual similarity scores based on the value of the respectivesimilarity scores of individual features. For instance, the automaticidentification system 100 may compute individual similarity scoresbetween respective minutiae between the search fingerprint and thereference fingerprint, and sort the individual similarity scores bytheir respective values.

A higher match score indicates a greater overall similarity between thesearch fingerprint and a reference record while a lower match scoreindicates a lesser over similarity between the search fingerprint and areference record. Therefore, the match (e.g., the relationship betweenthe search fingerprint and a reference record) with the highest matchscore is the match with the greatest relationship (based on minutiaecomparison) between the search fingerprint and the reference record.

Minutiae and OFV Matching

In general, the OFVs of minutiae may be compared between twofingerprints to determine a potential match between a referencefingerprint and a search fingerprint. The automatic fingerprintidentification system 100 may compare the OFVs of corresponding minutiaefrom the reference fingerprint and the search fingerprint to compute anindividual similarity score that reflects a confidence that theparticular reference minutiae corresponds to the particular searchminutiae that is being compared to. The automatic fingerprintidentification system 100 may then compute aggregate similarity scores,between a list of reference minutiae and a list of search minutiae,based the values of the individual similarity scores for each minutiae.For instance, as described more particularly below, various types ofaggregation techniques may be used to determine the aggregate similarityscores between the reference fingerprint and the search fingerprint.

FIGS. 5-7 generally describe different processes that may be used toduring fingerprint identification and matching procedures. For instance,FIG. 5 illustrates an exemplary process of calculating an individualsimilarity score between a reference minutia and a search minutia. FIG.6 illustrates an exemplary alignment process between two fingerprintsusing extracted minutiae from the two fingerprints, and FIG. 7illustrates an exemplary minutiae matching technique that may beemployed after the alignment procedure represented in FIG. 7. Asdescribed with respect to FIG. 8, the processes represented in FIGS. 5-7may be used in conjunction during an exemplary distorted fingerprintmatching process.

Referring to FIG. 5, a similarity determination process 500 may be usedto compute an individual similarity score between a reference minutia502 a from a reference fingerprint, and a search minutia 502 b from asearch fingerprint. A reference OFV 504 a and a search OFV 504 b may begenerated for the reference minutia 502 a and the search minutia 504 b,respectively, using the techniques described with respect to FIG. 4. Asshown, the search and reference OFVs 504 a and 504 b include individualoctant sectors k₀ to k₇ as described as illustrated in FIG. 3. Each ofthe reference OFV 504 a and the search OFV 504 b may include parameterssuch as, for example, the Euclidian distance 230, the minimum rotationangle 240, and the minimum rotation angle 250 as described with respectto FIG. 2. As shown in FIG. 5, exemplary parameters 506 a and 506 b mayrepresent the parameters for octant sector k₀ of the reference OFV 502 aand the search OFV 502 b, respectively.

The similarity score determination may be performed by an OFV comparisonmodule 520, which may be a software module or component of the automaticfingerprint identification system 100. In general, the similarity scorecalculation includes four steps. Initially, the Euclidian distancevalues of a particular octant sector may be evaluated (522).Corresponding sectors between the reference OFV 504 a and the similarityOFV 504 b may then be compared (524). A similarity score between aparticular octant sector within the reference OFV 504 a and itscorresponding octant sector in the search OFV 504 b may then be computed(526). Finally, the similarity scores for the between other octantsectors of the reference OFV 504 a and the search OFV 504 b may then becomputed and combined to generate the final similarity score between thereference OFV 504 a and the search OFV 504 b (528).

With respect to step 522, the similarity module 520 may initiallydetermine if the Euclidian distance values within the parameters 506 aand 506 b are non-zero values. For instance, as shown in decision point522 a, the Euclidean distance associated with the octant sector of thereference OFV 504 a, d _(RO), is initially be evaluated. If this valueis equal to zero, then the similarity score for the octant sector k₀ isset to zero. Alternatively, if the value of d_(RO) is greater than zero,then the similarity module 520 proceeds to decision point 522 b, wherethe Euclidean distance associated with the octant sector of thereference OFV 504 a, d_(SO), is evaluated. If the value of d_(SO) is notgreater than zero, then the OFV similarity module 520 evaluates thevalue of the Euclidean distance d_(S1), which is included in an adjacentsector k₁ to the octant sector k₀ within the reference OFV 504 b.Although FIG. 5 represents only one of the adjacent sectors beingselected, because each octant sector includes two adjacent octantsectors as shown in FIG. 3, in other implementations, octant sector k₇may also be evaluated. If the value of the Euclidean distance within theadjacent octant sector is not greater than zero, then the similaritymodule 520 sets the value of individual similarity score S_(RS01),between the octant sector k0 of the reference OFV 504 a and the octantsector k₁ of the reference OFV 504 b, to zero.

Alternatively, if the either the value of Euclidean distance d_(RO)within the octant sector k₀ of the reference OFV 504 a, or the Euclideandistance d_(S1) within the adjacent octant sector k₁ of the search OFV504 b is determined to be a non-zero value within the decision points522 b and 522 c, respectively, then the similarity module proceeds tostep 524.

In some instances, a particular octant vector may include zerocorresponding minutiae within the search OFV 504 b due to localizeddistortions within the search fingerprint. In such instances, where thecorresponding minutiae may have drifted to an adjacent octant sector,the similarity module 520 may alternatively compare the features of theoctant vector of the reference OFV 504 a to a corresponding adjacentoctant vector of the search OFV 504 b as shown in step 524 b.

If proceeding through decision point 522 b, the similarity module 520may proceed to step 524 a where the corresponding octant sectors betweenthe reference OFV 504 a and the search OFV 504 b are compared. Ifproceeding through decision point 522 c, the similarity module 620 mayproceed to step 524 b where the octant sector k₀ of the reference OFV504 a is compared to the corresponding adjacent octant sector k₁ thesearch OFV 504 b. During either process, the similarity module 520 maycompute the difference between the parameters that are included withineach octant sector of the respective OFVs. For instance, as shown, thedifference between the Euclidean distances 230, Δd, the differencebetween the minimum rotation angles 240, Δα, and the difference betweenthe minimum rotation angles 250, Δβ, may be computed. Since theseparameters represent geometric relationships between pairs of minutiae,the differences between them represent distance and orientationdifferences between the reference and search minutiae with respect toparticular octant sectors.

In some implementations, dynamic threshold values for the computedfeature differences may be used to handle nonlinear distortions withinthe search fingerprint in order to find mated minutiae between thesearch and reference fingerprint. For instance, the values of thedynamic thresholds may be adjusted to larger or smaller values to adjustthe sensitivity of the mated minutiae determination process. Forexample, if the value of the threshold for the Euclidean distance is setto a higher value, than more minutiae within a particular octant sectormay be determined to be a neighboring minutia to a reference minutiaebased on the distance being lower than the threshold value. Likewise, ifthe threshold is set to a smaller value, then a smaller number ofminutiae within the particular octant sector may be determined to beneighboring minutia based on the distance to the reference minutia beinggreater than the threshold value.

After either comparing the corresponding octant sectors in step 524 a orcomparing the corresponding adjacent octant sectors in step 524 b, thesimilarity module 520 may then compute an individual similarity scorebetween the respective octant sectors in steps 526 a and 526 b,respectively. For instance, the similarity score may be computed basedon the values of the feature differences as computed in steps 524 a and524 b. For instance, the similarity score may represent the featuredifferences and indicate minutiae that are likely to be distortedminutiae. For example, if the feature differences between a referenceminutiae and corresponding search minutia within a particular octantsector are close the dynamic threshold values, the similarity module 520may identify the corresponding search minutia as a distortion candidate.

After computing the similarity score for either the corresponding octantsectors, or the corresponding adjacent sectors in steps 526 a and 526 b,respectively, the similarity module 520 may repeat the steps 522-526 forall of the other octant sectors included within the reference OFV 504 aand the search OFV 504 b. For instance, the similarity module 520 mayiteratively execute the steps 522-526 until the similarity scoresbetween each corresponding octant sector and each corresponding adjacentoctant sector are computed for the reference OFV 504 a and the searchOFV 504 b.

The similarity module may then combine the respective similarity scoresfor each corresponding octant sectors and/or the corresponding adjacentoctant sectors to generate a final similarity score between thereference minutia and the corresponding search minutia. This finalsimilarity score is also referred to as the “individual similarityscore” between corresponding minutiae within the search and referencefingerprints as described in other sections of this specification. Theindividual similarity score indicates a strength of the local matchingof the corresponding OFV.

In some implementations, the particular aggregation technique used bythe similarity module 522 to generate the final similarity score (or the“individual similarity score”) may vary. For example, in some instances,the final similarity score may be computed based on adding the values ofthe similarity scores for the corresponding octant sectors and thecorresponding adjacent octant sectors, and normalizing the sum by a sumof a total number of possible mated minutiae for the reference minutiaand a total of number of possible mated minutiae for the search minutia.In this regard, the final similarity score is weighted by consideringthe number of mated minutiae and the total number of possible matedminutiae.

FIG. 6 illustrates an exemplary alignment process 600 between areference fingerprint and a search fingerprint. Briefly, the process 600may initially compare a list of reference OFVs 604 a associated with alist of reference minutiae 602 a and a list of search OFVs 604 bassociated with a list of search minutiae 604 b, and generate a list ofall possible mated minutiae 612. A global alignment module 620 may thenperform a global alignment procedure on the list of all possible matedminutia 612 to generate a clustered list of all possible mated minutiae622, and determine two best alignment rotations 622 for the searchfingerprint relative to the reference fingerprint. A precision alignmentmodule may then use the two best rotations 624 perform a secondalignment procedure to generate a list that includes the best-alignedpair 632 for the plurality of bins, which are provided as outputs of thealignment process.

As described previously with respect to FIG. 5, the OFVs ofcorresponding minutiae within the reference fingerprint and the searchfingerprint may be compared by the OFV comparison module 610 to generatethe list of all possible mated minutiae 612. As described, “matedminutiae” refer to a pair of minutiae that includes a particularreference minutia and a corresponding search minutia based at least onthe OFV comparison performed by the OFV comparison module 610, and thevalue of the individual similarity score between the two respective OFVsof the reference and search minutiae. The individual similarity scoreindicates a strength of the local matching of the corresponding OFV. Inaddition, the list of all possible mated minutiae 612 includes all ofthe minutiae within the octant sectors that are identified asneighboring minutiae to a particular reference minutia and have anon-zero similarity score, although additional mated minutiae may existwith similarity score values equal to zero. Although as shown in theFIG., the list of all possible minutiae 612 includes one search minutiaper reference minutia, in some instances, multiple mated minutiae mayexist within the list of all possible minutiae 612 for a singlereference minutia.

The global alignment module 620 performs a global alignment process onthe list of all possible mated minutiae 612, which estimates a probable(or best rotation) alignment between the reference fingerprint and thesearch fingerprint based on comparing the angle offsets between theindividual minutiae within the mated minutiae. For instance, the globalalignment module 620 may initially compute an angle offset for eachmated minutiae pair based on the individual similarity scores between aparticular search minutia and its corresponding reference minutia.

Each of the mated minutiae within the list of all possible matedminutiae 612 may then be grouped into a histogram bin that is associatedwith a particular angle offset range. For instance, two mated minutiaepairs within the list of all possible mated minutiae 612 may be groupedinto the same histogram bin if their respective individual similarityscores indicate a similar angular offset between the individual minutiawithin each mated minutiae pair. In some instances, the number ofhistogram bins for the list of all possible mated minutiae 612 is usedto estimate a fingerprint quality score for the search fingerprint. Forexample, if the quality of the search fingerprint is excellent, then theangular offset among each of the mated minutiae within the list of allpossible mated minutiae 612 should be consistent, and majority of themated minutiae will be grouped into a single histogram bin.Alternatively, if the fingerprint quality is poor, then the number ofhistogram bins would increase, representing significant variationsbetween the angular offset values between the mated minutiae within thelist of all possible mated minutiae 612.

In addition to grouping the mated minutiae into a particular histogrambin, the global alignment module 620 may determine a set of rigidtransformation parameters, which indicate geometric differences betweenthe reference minutiae and the search minutiae with similar angularoffsets. The rigid transformation parameters thus indicate a necessaryrotation of the search fingerprint at particular locations, representedby the locations of the minutiae, in order to geometrically align thesearch fingerprint to the reference fingerprint. Since the rigidtransformation parameters are computed for all possible mated minutiae,the necessary rotation represents a global alignment between thereference fingerprint and the search fingerprint. The global alignmentmodule 620 may then generate a clustered list of all possible matedminutiae, which groups the mated minutiae by the histogram bin based onthe respective angle offsets, and includes a set of rigid transformationparameters. In some implementations, the histogram represented by theplurality of bins may be smoothened by a Gaussian function.

The global alignment module 620 may use the clustered list of allpossible mated minutiae to determine two best rotations 624. Forinstance, the two best rotations 624 may be determined by using therigid transformation parameters to calculate a set of alignmentrotations for the search fingerprint using each histogram bin as areference point. Each alignment rotation may then be applied to thesearch fingerprint to generate a plurality of transformed searchfingerprints that is individually mapped to each alignment rotation. Forexample, in some instances, the number of alignment rotationscorresponds to the number of histogram bins generated for the list ofall possible mated minutiae 612. In such instances, the number oftransformed search fingerprints generated corresponds to the number ofhistogram bins included in the cluster list of all possible matedminutiae 622. Each set of transformed search fingerprints may then becompared to the reference fingerprint to determine the two bestrotations 624. For example, as described more particularly with respectto FIG. 7, each transformed search fingerprint may be compared to thereference fingerprint using a minutiae matching technique to determinewhich particular alignment rotations generate the greatest number ofcorrectly matched minutiae between a particular transformed searchfingerprint and the reference fingerprint. The global alignment module620 may then extract the two best alignment rotations 624, which arethen used by the precision alignment module 630.

In some implementations, different matching constraints may be used withthe minutiae matching techniques to determine the two best alignmentrotations 624.

The precision alignment module 630 may then use the two best alignmentrotations 624 to perform a precision alignment process that iterativelyrotates individual minutiae within the search fingerprint around the twobest alignment rotations 632 several times with small angle variationsto obtain a more precise pairing between individual search minutiae andtheir corresponding reference minutiae. For example, in some, twelverotations may be used with two degree angle variations. The minutiaethat are associated with the precise pairing between the searchfingerprint and the reference fingerprint are determined to be the listof best-aligned minutiae 632, which are provided for output by theprocess 600. The list of best-aligned minutiae 632 representtransformations of individual search minutiae within the searchfingerprint that most closely pair with the corresponding referenceminutiae of the reference fingerprint as a result of the globalalignment and the precision alignment processes.

FIG. 7 illustrates an exemplary minutiae matching process 700. Theminutiae matching process 700 may be performed after the fingerprintalignment process 600 as described in FIG. 6 to remove falsecorrespondences included within a list of aligned minutiae that isoutputted from alignment process. For instance, fingerprints from twofingers of an individual may share local structures, which can result infalse correspondence minutiae between a search fingerprint of one fingerand a reference fingerprint of another finger. To resolve this, theminutiae matching process 700 includes a two-stage pruning process toremove false correspondence minutiae pairs within a list of all possiblemated minutiae.

Briefly, the process 700 may include a local geometric module 710receiving a list of aligned minutiae 710, and generating a modified listof all possible minutiae 712 that does not include false correspondenceminutiae. A global consistency pairing module may then sort the modifiedlist of all possible minutiae 712 by values of the respective individualsimilarity scores to generate a sorted modified list of all possibleminutiae 722. The global consistency pairing module 720 may removeminutiae pairs with duplicate indexes 724, and group the list of matedminutiae based on conducting a global geometric consistency evaluationto generate a list of geometrically consistent groups 726, which arethen outputted with a top average similarity score from one of thegeometrically consistent groups.

Initially, the local geometric module 710 may select the best-pairedminutiae from the list of all possible mated minutiae. For instance,after aligned the search fingerprint and the reference fingerprint asdescribed in FIG. 6, the local geometric module 710 may scan the list ofall possible minutiae and identify the two minutiae pairs with theminimum orientation difference.

In some instances, the identification of the best-paired minutiae mayadditionally be subject to satisfying a set of constraints. For example,one constraint may be that the index of the first pair is different fromthat of the first pair. In other examples, the rigid transformationparameters of the two best-paired minutiae pairs may be compared tothreshold values to ensure that the two identified pairs aregeometrically consistent.

After identifying the two best-paired minutiae pairs, the localgeometric module 710 may use the two best-paired minutiae pairs asreference pairs to remove other minutiae pairs from the list of allpossible mated minutiae. In some instances, particular pairs may beremoved if they satisfy one or more removal criteria based on theattributes of the two best paired minutiae pairs. For example, oneconstraint may be that if the minutiae index of a particular pair is thesame as one of the best-paired minutiae pairs, then that particular pairmay be identified as a duplicate within the list of all possibleminutiae and removed as a false correspondence. In another example, arotational constraint may be used to remove particular minutiae pairsthat have a large orientation difference compared to the two best-pairedminutiae pairs. In another example, distance constraints may be used tokeep each particular minutiae pair within the list of all possible matedminutiae geometrically consistent with the two best-paired minutiaepairs. The updated list of minutiae pairs that is generated is themodified list of all possible mated minutiae 712.

After the modified list of all possible mated minutiae is generated, theglobal consistency pairing module 720 may perform a global consistencypairing operation on the modified list of all possible mated minutiae722 to generate the list of geometrically-consistent groups 726 (or“globally aligned mated minutiae”). For instance, the global consistencypairing module 720 may initially sort the list modified list of allpossible mated minutiae 712 by the similarity score, and then scan thelist and remove particular minutiae pairs 724 that have minutiae indexesthat are similar to the minutiae pairs with the highest similarityscores in the sorted modified list of all possible mated minutiae 722.

The global consistency pairing module 720 may then initialize a set ofgroups based on the number of reference minutiae included within thelist. For instance, a group may be created for each reference minutiasuch that if there are multiple minutiae pairs within the list of sortedmodified list of all possible minutiae 722 for a single referenceminutia, the multiple minutiae pairs are included in the same group. Insome instances, the global consistency pairing module 720 mayadditionally check the geometric consistency between each of theminutiae pairs within the same group and remove minutiae pairs that aredetermined not be geometrically consistent. The global consistencypairing module 720 may then compute an average similarity score for eachgroup based on aggregating the individual similarity scores associatedwith each of the minutiae pairs within the group.

After computing the average similarity scores for each group, the globalconsistency pairing module 720 may then compare the average similarityscores between each group and select the group that has the highestaverage similarity score and then provide the list of minutiae that areincluded within the group for output of the process 700 and include thetop average similarity score.

As describe above, FIGS. 5-7 illustrate processes that are utilized bythe automatic fingerprint identification system to process, analyze, andmatch individual minutiae from a search fingerprint to a referencefingerprint. As described in FIG. 8, these processes may be utilizedwithin a matching operation for a distorted fingerprint to compute amatch score that indicates a confidence of a fingerprint match between asearch fingerprint and a reference fingerprint.

Generating Virtual Minutiae

As described above, the mated minutiae may be identified based on thecomparison between the OFVs and a determination of similarity scoresafter an alignment of the search fingerprint and reference fingerprintis performed. Once the alignment of the search and reference fingerprintis performed, as described above, an overlapping area of the area ofsearch fingerprint and reference record may be determined. Further, uponalignment of the search and reference fingerprints, virtual minutiae maybe applied to match the no-minutiae areas and the sparse minutiae areas.

As described above, such incorporation of virtual minutiae may allowautomatic fingerprint detection system 100 to fuse information fromno-minutiae areas and sparse minutiae areas into an aggregate similarityscore. To clarify, the aggregate similarity score may be calculated asthe cumulative of individual similarities between the mated minutiaepairs.

Referring to FIG. 8, a method for analyzing the similarity between asearch fingerprint and a file fingerprint is shown. The automaticfingerprint detection system 100 may initially align the searchfingerprint and reference fingerprint using the methods described withrespect to FIG. 6 (810). In addition, the automatic fingerprintdetection system 100 may apply a regular grid to an overlapping regioncontaining both the search fingerprint and the reference fingerprint(820). In some implementations, the regular grid is a grid with squareshaving a length of thirty two pixels. In other implementations, anysuitable grid regularity may be applied.

The automatic fingerprint detection system 100 may further apply avirtual minutiae to the search and reference fingerprint based on thegrid regularity (830). For instance, the automatic fingerprint detectionsystem 100 may generate a list of virtual minutiae for each of thesearch and file fingerprints. Each of the virtual minutiae have the sameorientation or direction (e.g., zero degrees).

The automatic fingerprint detection system 100 may designate thecorresponding virtual minutiae for each search fingerprint and referencefingerprint as mated virtual pairs (840). For instance, such matedvirtual pairs may be used for similarity score calculations as describedpreviously with respect to FIG. 5.

Automatic fingerprint detection system 100 may generate a minutiadescriptor (e.g., the OFVs) for each of the virtual minutiae (850). Forinstance, using the generated minutiae descriptors, automaticfingerprint detection system 100 may compare the associated localminutiae descriptors with a particular mated virtual minutiae pair tocompute a virtual similarity score (or a second similarity score) (860).Finally, the automatic fingerprint detection system 100 may aggregatethe scores of all mated virtual minutiae pairs to compute to a finalsimilarity score (870).

FIG. 9 illustrates an exemplary set of virtual minutiae generated for afingerprint. As depicted in FIG. 9, an illustration 900 shows a searchfingerprint region 910 containing search minutiae that is aligned andoverlaid with file fingerprint region 920 such that overlapping region930 is created. As described previously with respect to FIG. 8, aregular grid 940 may be overlaid to allow for the generation of virtualminutiae 951-961 at regular intervals (e.g., at the intersections ofregular grid 940). As shown, virtual minutiae are represented astriangles 950 in the overlapping region 930 and each virtual minutiae isassociated with an orientation of zero degrees.

By using the virtual minutiae shown in FIG. 9, the automatic fingerprintdetection system 100 may analyze the quality of matching in regions withsparse minutiae or no-minutiae. Specifically, automatic fingerprintdetection system 100 may re-compute the similarity score between matedand non-mated minutiae between the search and reference fingerprintsbased on a number of factors such as, for example, the number of virtualminutiae, and the similarity between the virtual mated minutiae pairs.For instance, the virtual similarity score may be obtained by matchinglocal minutia descriptor the virtual mated minutiae pairs. By processingthe virtual similarity score based on amount of overlapping spacebetween the search and file fingerprints (e.g., because the count ofvirtual minutiae acts as a proxy for this space), matches based onnegative space (e.g., space that “matches” while not containingsignificant amounts of minutiae) may be incorporated into the similarityscore to improve match quality.

FIG. 10 illustrates an exemplary process 1000 of matching fingerprintsusing virtual minutiae. Briefly, the process 1000 may include receivinga first similarity score (1010), generating a list of virtual searchminutiae and a list of virtual file minutiae (1020), computing a secondsimilarity score (1030), computing a fused similarity score (1040), andproviding the fused similarity score for output (1040).

In more detail, the automatic fingerprint detection system 100determines a first similarity score between a list of search minutiaefrom a search fingerprint and a list of file minutiae from a referencefingerprint (1010). As described above, in some implementations, each ofthe list of search minutiae and the list of file minutiae include alocation identifier and an orientation identifier.

In other implementations, the automatic fingerprint detection systemcomputes the first similarity score between the search fingerprint andthe reference fingerprint by comparing the first list of minutiae to thesecond list of minutiae using the methods described above and throughoutthis specification. Specifically, the automatic fingerprint detectionsystem 100 may compute the first similarity score by generating aplurality of search OFVs associated with the first list of minutiae anda plurality of file OFVs associated with the second list of minutiae.For instance, the automatic fingerprint detection system 100 may computethe first similarity score by pairing each of the first list of minutiaewith each of the second list of minutiae to create a plurality of allpossible mated minutiae such that each possible mated minutiae includesan associated first minutiae from the first list of minutiae and anassociated second minutiae from the second list of minutiae. Inaddition, the automatic fingerprint detection system 100 may compute thefirst similarity score by comparing a first search OFV associated withthe associated first minutiae to a first file OFV associated with theassociated second minutiae as described above.

In at least some examples, the automatic fingerprint detection system100 computes the first similarity score by applying local and globalmatching processes. For instance, the local matching processes mayinclude finding the best matched pair for each minutia point. In otherinstances, the global matching processes may include finding a maximumnumber of corresponding minutiae from the local matched minutia pairs.

The automatic fingerprint detection system 100 may generate a list ofvirtual search minutiae for the list of search minutiae, and a list ofvirtual file minutiae for the list of file minutiae (1030), As describedpreviously, in some implementations, the automatic fingerprint detectionsystem 100 identifies an overlapping region containing a portion of thesearch fingerprint and a portion of the file fingerprint. The automaticfingerprint detection system 100 may apply a coordinate grid including aplurality of cross points to the overlapping region, and then theautomatic fingerprint detection system 100 may place a virtual minutiae,for each of the list of search minutiae and the list of file minutiae,on each of the plurality of cross points of the applied regular gridover the overlapping region. Alternatively, in some examples, theautomatic fingerprint detection system 100 may generate two polygonsfrom the list of file minutiae and the list of search minutiae andoverlay the two polygons based on the alignment of two correspondentminutiae list. In each implementation, generating the list of virtualsearch minutiae and the list of virtual file minutiae may includedetermining a number of virtual minutia to create from the overlap areabased in part on the regularity of the grid applied.

The automatic fingerprint detection system 100 may compute a secondsimilarity score based at least on comparing the list of virtual searchminutiae and the list of virtual file minutiae (1030). As describedpreviously, in some implementations, computing the second similarityscore may be performed as described in reference to FIG. 8. In otherexamples, the minutiae comparing the list of virtual search minutiae andthe list of virtual file minutiae may be substantially similar to thematching process applied to the list of search minutiae and the list offile minutiae as described previously in FIG. 7.

The automatic fingerprint detection system 100 may compute a fusedsimilarity score between search fingerprint and the referencefingerprint based at least on combining the first similarity score andthe second similarity score (1040). As described previously, in someimplementations, the fused similarity score indicates a match qualitybetween the search fingerprint and the reference fingerprint. In someexamples, the fused similarity score may be computed by using a linearcombination. In other examples, the fused similarity score may becomputed by using any other fusion method.

The automatic fingerprint detection system 100 may provide the fusedsimilarity score for output (1050). In some implementations, the fusedsimilarity score may be transmitted to a particular component of theautomatic fingerprint detection system 100 that determines a fingerprintmatch between the search fingerprint and the reference fingerprint.

Applications of Virtual Minutia Matching

As described above, virtual minutiae may be generated and used to matchfingerprints using a combined similarity score based on the firstsimilarity score between a first and second list of minutiae and thesecond similarity score between the third and fourth list of virtualminutiae. Specifically, techniques that match fingerprints based on thevirtual minutiae may be used in applications where minimal informationsuch as minutiae are available. For example, the techniques describedabove may be implemented to improve the accuracy of fingerprint matchingwithout requiring any additional feature extraction information from theoriginal image.

In some implementations, the techniques described above may be used tomatch fingerprints that include significant background noise. Forexample, the techniques may be implemented for use with fingerprintswithin a search fingerprint that include a large number of low qualityfeatures that increase the probability of generating false positives infingerprint matching. In such examples, the systems described above mayinitially determine if the fingerprints included in the searchfingerprint are likely to cause false positives in matching based ondetermining the quality of features extracted from the OFV. In responseto determining that the fingerprints include low quality features, thesystems may use the techniques described above to generate a list ofvirtual minutiae of the fingerprints with low quality features and usethe fingerprint matching technique based on the virtual minutiae as aconfirmatory process for the initial fingerprint matching techniquebased on actual minutiae.

In other implementations, the techniques described above may be used incomputationally-limited environments where minimal resources areavailable to an automatic fingerprint detection system in order togenerate an accurate fingerprint match. For example, the techniques maybe implemented for use with systems where large number of features arerequired to be extracted from the fingerprints to generate asufficiently accurate fingerprint match. In such examples, thetechniques that use virtual minutiae to match fingerprints may enhancethe computational efficiency of such systems by requiring lower numberof features to be extracted into the OFV in order to generate asufficiently accurate fingerprint match. For instance, insteadgenerating a fixed number of features for each fingerprint from thesearch fingerprint, the techniques may instead use the generated virtualminutiae to redundantly compare each fingerprint against an initialcomparison to reduce the number of required features to generate anaccurate fingerprint match result. This reduces the computational burdenof the automatic fingerprint detection system by reducing the resourcesrequired for the OFV determination process as previously described.

Described throughout this specification are computer systems such asautomatic fingerprint identification systems and user computer systems.As described throughout this specification, all such computer systemsinclude a processor and a memory. However, any processor in a computerdevice referred to throughout this specification may also refer to oneor more processors throughout this specification the processor may be inone computing device or a plurality of computing devices acting inparallel. Additionally, any memory in a computer device referred tothroughout this specification may also refer to one or more memoriesthroughout this specification the memories may be in one computingdevice or a plurality of computing devices acting in parallel.

As used throughout this specification, a processor may include anyprogrammable system including systems using micro-controllers, reducedinstruction set circuits (RISC), application specific integratedcircuits (ASICs), logic circuits, and any other circuit or processorcapable of executing the functions described throughout thisspecification. The above examples are example only, and are thus notintended to limit in any way the definition and/or meaning of the term“processor.”

As used throughout this specification, the term “database” may refer toeither a body of data, a relational database management system (RDBMS),or to both. As used throughout this specification, a database mayinclude any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. The above examplesare example only, and thus are not intended to limit in any way thedefinition and/or meaning of the term database. Examples of RDBMS'sinclude, but are not limited to including, Oracle® Database, MySQL, IBM®DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, anydatabase may be used that enables the systems and methods describedthroughout this specification. (Oracle is a registered trademark ofOracle Corporation, Redwood Shores, Calif.; IBM is a registeredtrademark of International Business Machines Corporation, Armonk, N.Y.;Microsoft is a registered trademark of Microsoft Corporation, Redmond,Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.).

As used throughout this specification, “minutia” may refer to majorfeatures that are extracted from a fingerprint which may be used by anautomated fingerprint matching system for comparisons between areference fingerprint and a search fingerprint. For example, theminutiae may include features that include abrupt endings of a ridge, asingle ridge that divides into two ridges, a ridge that commences, andtravels a short distance before ending, a single ridge ending that isnot connected to all other ridges, a single ridge that bifurcates andreunites shortly afterward to continue as a single ridge, or other typesof common features used within conventional fingerprint matchingtechniques.

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an example embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality. In some embodiments, the systemincludes multiple components distributed among a plurality of computingdevices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.

As used throughout this specification, an element or step recited in thesingular and proceeded with the word “a” or “an” should be understood asnot excluding plural elements or steps, unless such exclusion isexplicitly recited. Furthermore, references to “example embodiment” or“one embodiment” of the present disclosure are not intended to beinterpreted as excluding the existence of additional embodiments thatalso incorporate the recited features.

As used throughout this specification, the terms “software” and“firmware” are interchangeable, and include any computer program storedin memory for execution by a processor, including RAM memory, ROMmemory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM)memory. The above memory types are example only, and are thus notlimiting as to the types of memory usable for storage of a computerprogram.

It should be understood that processor as used throughout thisspecification means one or more processing units (e.g., in a multi-coreconfiguration). The term processing unit, as used throughout thisspecification, refers to microprocessors, microcontrollers, reducedinstruction set circuits (RISC), application specific integratedcircuits (ASIC), logic circuits, and any other circuit or device capableof executing instructions to perform functions described throughout thisspecification.

It should be understood that references to memory mean one or moredevices operable to enable information such as processor-executableinstructions and/or other data to be stored and/or retrieved. Memory mayinclude one or more computer readable media, such as, withoutlimitation, hard disk storage, optical drive/disk storage, removabledisk storage, flash memory, non-volatile memory, ROM, EEPROM, randomaccess memory (RAM), and the like.

Additionally, it should be understood that communicatively coupledcomponents may be in communication through being integrated on the sameprinted circuit board (PCB), in communication through a bus, throughshared memory, through a wired or wireless data communication network,and/or other means of data communication. Additionally, it should beunderstood that data communication networks referred to throughout thisspecification may be implemented using Transport ControlProtocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), orthe like, and the underlying connections may comprise wired connectionsand corresponding protocols, for example, Institute of Electrical andElectronics Engineers (IEEE) 802.3 and/or wireless connections andassociated protocols, for example, an IEEE 802.11 protocol, an IEEE802.15 protocol, and/or an IEEE 802.16 protocol.

A technical effect of systems and methods described throughout thisspecification includes at least one of: (a) retrieving a searchfingerprint from a fingerprint scanning device containing a first listof minutiae; (b) retrieving a reference fingerprint from a databasecontaining a second list of minutiae; (c) computing a first similarityscore between the search fingerprint and the reference fingerprint bycomparing the first list of minutiae to the second list of minutiae; (d)generating a third list of virtual minutiae in the search fingerprintand a fourth list of virtual minutiae in the reference fingerprint; (e)computing a second similarity score between the search fingerprint andthe reference fingerprint by comparing the third list of virtualminutiae to the fourth list of virtual minutiae; and (f) computing athird combined similarity score based on the first similarity score andthe second similarity score, were this specification the third combinedsimilarity score indicates a match quality between the searchfingerprint and the reference fingerprint.

Exemplary embodiments of systems and method for matching of distortedfingerprints are described above in detail. The methods and systems arenot limited to the specific embodiments described throughout thisspecification, but rather, components of systems and/or steps of themethods may be utilized independently and separately from othercomponents and/or steps described throughout this specification. Forexample, the methods may also be used in combination with other imagingsystems and methods, and are not limited to practice with only thesystems as described throughout this specification.

Although specific features of various embodiments of the invention maybe shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the invention, any feature ofa drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A method for matching fingerprints using virtualminutiae, the method comprising: obtaining data indicating (i) a list ofsearch minutiae from a search fingerprint and (ii) a list of referenceminutiae from a reference fingerprint; determining a matched area sharedbetween the search fingerprint image and the reference fingerprint basedon the list of search minutiae and the list of reference minutiae;generating a list of virtual search minutiae for the list of searchminutiae based on the matched area of the search fingerprint image;generating a list of virtual reference minutiae for the list ofreference minutiae based on the matched area of the referencefingerprint image; and computing a similarity score between the searchfingerprint and the reference fingerprint based at least on comparingthe list of virtual search minutiae and the list of virtual searchminutiae.
 2. The method of claim 1, further comprising: generating (i) aset of search octant feature vectors for each search minutia from thelist of search minutiae, and (ii) a set of reference octant featurevectors for each reference minutia from the list of reference minutiae;generating a plurality of possible mated minutiae based at least onpairing an octant neighborhood of each of the list of search minutiaewith the octant neighborhood of each of the list of reference minutiae,wherein: the octant neighborhood includes a combination of (i) aneighborhood formed between the list of virtual search minutiae and thelist of search minutiae, and (ii) a neighborhood formed between the listof virtual reference minutiae and the list reference minutiae, and eachof the plurality of possible mated minutiae includes (i) a searchminutia from the list of search minutiae, and (ii) a reference minutiafrom the list of reference minutiae; and wherein the similarity scorebetween the search fingerprint image and the reference fingerprint imageis based on comparing minutiae pairs of the plurality of possible matedminutiae.
 3. The method of claim 1, further comprising: identifying anoverlapping region containing a portion of the search fingerprint and aportion of the reference fingerprint; applying a coordinate gridincluding a plurality of cross points to the overlapping region; placinga virtual search minutia for the list of search minutiae on each of theplurality of cross points of the applied regular grid over theoverlapping region; placing a virtual reference minutia for the list ofreference minutiae on each of the plurality of cross points of theapplied regular grid over the overlapping region; and generating a setof virtual search octant feature vectors for each virtual search minutiafrom the list of virtual search minutiae; generating a set of virtualreference octant feature vectors for each virtual reference minutia fromthe list of virtual reference minutiae.
 4. The method of claim 3,wherein generating the list of virtual minutiae for each of the list ofsearch minutiae and the list of file minutiae is based at least on adensity of the plurality of cross points within the overlapping region.5. The method of claim 1, wherein computing the similarity score betweenthe search fingerprint and the reference fingerprint comprises:computing a first score between the list of search minutiae and the listof reference minutiae, the first score representing a similarity betweenthe list of search minutiae and the list of reference minutiae;computing a second score based at least on comparing the list of virtualsearch minutiae and the list of virtual reference minutiae, the secondscore representing a similarity between the list of virtual searchminutiae and the list of virtual reference minutiae; and combining thefirst score and the second score to compute the similarity score.
 6. Themethod of claim 1, wherein combining the first score and the secondscore comprises assigning respective weights to each the first score andthe second score.
 7. The method of claim 6, wherein assigning therespective weights to each of the first and second scores comprisesassigning a respective linear weights to each of the first and secondscores.
 8. A system comprising: one or more computers; and one or morestorage devices storing instructions that, when executed by the one ormore computers, cause the one or more computers to perform operationscomprising: obtaining data indicating (i) a list of search minutiae froma search fingerprint and (ii) a list of reference minutiae from areference fingerprint; determining a matched area shared between thesearch fingerprint image and the reference fingerprint based on the listof search minutiae and the list of reference minutiae; generating a listof virtual search minutiae for the list of search minutiae based on thematched area of the search fingerprint image; generating a list ofvirtual reference minutiae for the list of reference minutiae based onthe matched area of the reference fingerprint image; and computing asimilarity score between the search fingerprint and the referencefingerprint based at least on comparing the list of virtual searchminutiae and the list of virtual search minutiae.
 9. The system of claim8, wherein the operations further comprise: generating (i) a set ofsearch octant feature vectors for each search minutia from the list ofsearch minutiae, and (ii) a set of reference octant feature vectors foreach reference minutia from the list of reference minutiae; generating aplurality of possible mated minutiae based at least on pairing an octantneighborhood of each of the list of search minutiae with the octantneighborhood of each of the list of reference minutiae, wherein: theoctant neighborhood includes a combination of (i) a neighborhood formedbetween the list of virtual search minutiae and the list of searchminutiae, and (ii) a neighborhood formed between the list of virtualreference minutiae and the list reference minutiae, and each of theplurality of possible mated minutiae includes (i) a search minutia fromthe list of search minutiae, and (ii) a reference minutia from the listof reference minutiae; and wherein the similarity score between thesearch fingerprint image and the reference fingerprint image is based oncomparing minutiae pairs of the plurality of possible mated minutiae.10. The system of claim 8, wherein the operations further comprise:identifying an overlapping region containing a portion of the searchfingerprint and a portion of the reference fingerprint; applying acoordinate grid including a plurality of cross points to the overlappingregion; placing a virtual search minutia for the list of search minutiaeon each of the plurality of cross points of the applied regular gridover the overlapping region; placing a virtual reference minutia for thelist of reference minutiae on each of the plurality of cross points ofthe applied regular grid over the overlapping region; and generating aset of virtual search octant feature vectors for each virtual searchminutia from the list of virtual search minutiae; generating a set ofvirtual reference octant feature vectors for each virtual referenceminutia from the list of virtual reference minutiae.
 11. The system ofclaim 10, wherein generating the list of virtual minutiae for each ofthe list of search minutiae and the list of file minutiae is based atleast on a density of the plurality of cross points within theoverlapping region.
 12. The system of claim 8, wherein computing thesimilarity score between the search fingerprint and the referencefingerprint comprises: computing a first score between the list ofsearch minutiae and the list of reference minutiae, the first scorerepresenting a similarity between the list of search minutiae and thelist of reference minutiae; computing a second score based at least oncomparing the list of virtual search minutiae and the list of virtualreference minutiae, the second score representing a similarity betweenthe list of virtual search minutiae and the list of virtual referenceminutiae; and combining the first score and the second score to computethe similarity score.
 13. The system of claim 8, wherein combining thefirst score and the second score comprises assigning respective weightsto each the first score and the second score.
 14. The system of claim13, wherein assigning the respective weights to each of the first andsecond scores comprises assigning a respective linear weights to each ofthe first and second scores.
 15. A non-transitory computer-readablestorage device encoded with computer program instructions that, whenexecuted by one or more computers, cause the one or more computers toperform operations comprising: obtaining data indicating (i) a list ofsearch minutiae from a search fingerprint and (ii) a list of referenceminutiae from a reference fingerprint; determining a matched area sharedbetween the search fingerprint image and the reference fingerprint basedon the list of search minutiae and the list of reference minutiae;generating a list of virtual search minutiae for the list of searchminutiae based on the matched area of the search fingerprint image;generating a list of virtual reference minutiae for the list ofreference minutiae based on the matched area of the referencefingerprint image; and computing a similarity score between the searchfingerprint and the reference fingerprint based at least on comparingthe list of virtual search minutiae and the list of virtual searchminutiae.
 16. The device of claim 15, wherein the operations furthercomprise: generating (i) a set of search octant feature vectors for eachsearch minutia from the list of search minutiae, and (ii) a set ofreference octant feature vectors for each reference minutia from thelist of reference minutiae; generating a plurality of possible matedminutiae based at least on pairing an octant neighborhood of each of thelist of search minutiae with the octant neighborhood of each of the listof reference minutiae, wherein: the octant neighborhood includes acombination of (i) a neighborhood formed between the list of virtualsearch minutiae and the list of search minutiae, and (ii) a neighborhoodformed between the list of virtual reference minutiae and the listreference minutiae, and each of the plurality of possible mated minutiaeincludes (i) a search minutia from the list of search minutiae, and (ii)a reference minutia from the list of reference minutiae; and wherein thesimilarity score between the search fingerprint image and the referencefingerprint image is based on comparing minutiae pairs of the pluralityof possible mated minutiae.
 17. The device of claim 16, wherein theoperations further comprise: identifying an overlapping regioncontaining a portion of the search fingerprint and a portion of thereference fingerprint; applying a coordinate grid including a pluralityof cross points to the overlapping region; placing a virtual searchminutia for the list of search minutiae on each of the plurality ofcross points of the applied regular grid over the overlapping region;placing a virtual reference minutia for the list of reference minutiaeon each of the plurality of cross points of the applied regular gridover the overlapping region; and generating a set of virtual searchoctant feature vectors for each virtual search minutia from the list ofvirtual search minutiae; generating a set of virtual reference octantfeature vectors for each virtual reference minutia from the list ofvirtual reference minutiae.
 18. The device of claim 17, whereingenerating the list of virtual minutiae for each of the list of searchminutiae and the list of file minutiae is based at least on a density ofthe plurality of cross points within the overlapping region.
 19. Thedevice of claim 15, wherein computing the similarity score between thesearch fingerprint and the reference fingerprint comprises: computing afirst score between the list of search minutiae and the list ofreference minutiae, the first score representing a similarity betweenthe list of search minutiae and the list of reference minutiae;computing a second score based at least on comparing the list of virtualsearch minutiae and the list of virtual reference minutiae, the secondscore representing a similarity between the list of virtual searchminutiae and the list of virtual reference minutiae; and combining thefirst score and the second score to compute the similarity score. 20.The device of claim 15, wherein combining the first score and the secondscore comprises assigning respective weights to each the first score andthe second score.